The article addresses the issue of educational development policy in Ukraine: the main trends and ways, means, technologies of their implementation. It has been observed that educational policy is developing and changing under the influence of such factors as Russia’s military actions against our country, European integration and globalisation. It has been taken into account that globalisation trends in the world integration, according to which globalisation processes should be reflected not only in the foreign economic, political or technological spheres, but also, as a consequence, in the development of technologies for training future teachers. Integration of digital technologies in the educational process is one of the key tendencies in the modern educational policy in Ukraine. The characteristics of the most used technologies of augmented reality in the modern school of Ukraine have been outlined. The algorithm for displaying generalized information about a particular application was proposed, namely: payment, accessibility, language, system requirements; learning opportunities; practical value; website; video about the application. The model of the formation of future teachers’ skills to use augmented reality technologies in the process of natural sciences studying has been proposed. We consider it as a component of a holistic system of future teachers’ professional training. The conceptual basis for the development of the model is a multi-subject educational paradigm, which is considered to be open, self-developing and self-organizing, causing a fundamental change in the behavior and relationships of the educational process participants. The proposed model is implemented in the authors’ methodological system, which ensures the interconnected activities of all participants in the educational process. Its systemic factor is the goal of improving the quality of the future natural sciences teachers’ professional training by developing their skills in using AR technology. The end result is an increase in the level of future natural sciences teachers’ readiness to use AR technology in their professional activities.

This study thoroughly examined the use of different machine learning models to predict financial distress in Indonesian companies by utilizing the Financial Ratio dataset collected from the Indonesia Stock Exchange (IDX), which includes financial indicators from various companies across multiple industries spanning a decade. By partitioning the data into training and test sets and utilizing SMOTE and RUS approaches, the issue of class imbalances was effectively managed, guaranteeing the dependability and impartiality of the model’s training and assessment. Creating first models was crucial in establishing a benchmark for performance measurements. Various models, including Decision Trees, XGBoost, Random Forest, LSTM, and Support Vector Machine (SVM) were assessed. The ensemble models, including XGBoost and Random Forest, showed better performance when combined with SMOTE. The findings of this research validate the efficacy of ensemble methods in forecasting financial distress. Specifically, the XGBClassifier and Random Forest Classifier demonstrate dependable and resilient performance. The feature importance analysis revealed the significance of financial indicators. Interest_coverage and operating_margin, for instance, were crucial for the predictive capabilities of the models. Both companies and regulators can utilize the findings of this investigation. To forecast financial distress, the XGB classifier and the Random Forest classifier could be employed. In addition, it is important for them to take into account the interest coverage ratio and operating margin ratio, as these finansial ratios play a critical role in assessing their performance. The findings of this research confirm the effectiveness of ensemble methods in financial distress prediction. The XGBClassifier and RandomForestClassifier demonstrate reliable and robust performance. Feature importance analysis highlights the significance of financial indicators, such as interest coverage ratio and operating margin ratio, which are crucial to the predictive ability of the models. These findings can be utilized by companies and regulators to predict financial distress.

The rise of digital communication technologies has significantly changed how people participate in social protests. Digital platforms—such as social media—have enabled individuals to organize and mobilize protests on a global scale. As a result, there has been a growing interest in understanding the role of digital communication in social protests. This manuscript provides a comprehensive bibliometric analysis of the evolution of research on digital communication and social protests from 2008 to 2022. The study employs bibliometric methodology to analyze a sample of 260 research articles extracted from the SCOPUS core collection. The findings indicate a significant increase in scholarly investigations about digital communication and its role in social protest movements during the past decade. The number of publications on this topic has increased significantly since 2012—peaking in 2022—indicating a heightened interest following COVID-19. The United States, United Kingdom, and Spain are the leading countries in publication output on this topic. The analysis underlines scholars employing a range of theoretical perspectives—including social movement theory, network theory, and media studies—to identify the relationship between digital communication and social protests. Social media platforms—X (Twitter), Facebook, and YouTube—are the most frequently studied and utilized digital communication tools engaged in social protests. The study concludes by identifying emerging topics relating to social movements, political communication, and protest, thereby suggesting gaps and opportunities for future research.

The electoral campaign that led Trump to win the presidential election focused on attacking the elites and using nationalist rhetoric, highlighting issues such as illegal immigration and economic globalization. Once in power, his trade policies, based on perceptions of unfair competition with countries like China, resulted in the imposition of high tariffs on key products. These measures were justified as necessary to protect domestic industries and jobs, although they triggered trade wars at the international level. This article examines the economic consequences of the protectionist policies implemented by the United States under the Trump administration. The protection of less competitive sectors aims to reduce imports, negatively affecting production and income in exporting countries, and limiting U.S. exports to these markets. Although some countries have experienced an increase in real income due to trade diversion, overall, income fluctuations have been negative.

This paper utilizes an advanced Network Data Envelopment Analysis (DEA) model to examine the impact of mobile payment on the efficiency of Taiwan banking industry. Inheriting the literature, we separate the banking operation process into two stages, namely profitability and marketability. Mobile payment is then considered as the core factor in the second stage. Our paper discovers network DEA model can effectively enhance the analysis of banking industry’s efficiency, and mobile payment has a notable impact on Taiwan banking industry. Regarding the profitability stage, there is only one efficient bank in 2019 and 2022, respectively. These banks also perform better in terms of “mobile payment production”. In the marketability stage, there is also only one bank in 2021 and one bank in 2022, that can reach to unique efficiency score. This indicates many banks attempt to increase earnings per share through investing in mobile payment services. However, the achievement still needs more wait. This leads to the fact that no bank can reach the ultimate overall efficiency. Within our sample, we also find that regarding promoting mobile payment services, Private Banks outperform Government Banks.